Online services can increase their popularity if user interaction can be intelligently processed to make recommendations to the user. For example, an application running on a web page server may recommend additional webpages to a user based upon their current usage. In that regard, substantial encyclopedic databases currently exist such as Wikipedia that provide a vast amount of information regarding concepts such as “New York” or “Madonna” and so on. If such databases could be automatically analyzed to determine the relationship between concepts, recommendations to the user become more accurate, which supports user interaction and engagement.
Because the discovery of relationships between concepts in an online database is thus so valuable, considerable efforts have been expended attempting to provide such relationship analyses. Unfortunately, however, current analyses have been developed only on a case-by-case basis with regard to specific databases. Such applications are generally valid only for a single language usage and difficult or impossible to transfer to other languages. Update of resources in conventional applications with regard to concept relationships thus involves a substantial amount of manual intervention.
There is thus a need in the art for an application that can process a database to discover relationships between concepts in the database.